Overview

Dataset statistics

Number of variables29
Number of observations6411
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory281.2 B

Variable types

Categorical8
Boolean8
Numeric13

Alerts

Customer ID has a high cardinality: 6411 distinct valuesHigh cardinality
State has a high cardinality: 51 distinct valuesHigh cardinality
Phone Number has a high cardinality: 6402 distinct valuesHigh cardinality
Account Length (months) is highly overall correlated with Local Calls and 2 other fieldsHigh correlation
Local Calls is highly overall correlated with Account Length (months) and 2 other fieldsHigh correlation
Local Mins is highly overall correlated with Account Length (months) and 2 other fieldsHigh correlation
Intl Calls is highly overall correlated with Intl Mins and 2 other fieldsHigh correlation
Intl Mins is highly overall correlated with Intl Calls and 2 other fieldsHigh correlation
Extra International Charges is highly overall correlated with Intl Calls and 2 other fieldsHigh correlation
Customer Service Calls is highly overall correlated with Churn LabelHigh correlation
Avg Monthly GB Download is highly overall correlated with Under 30High correlation
Age is highly overall correlated with Under 30 and 1 other fieldsHigh correlation
Number of Customers in Group is highly overall correlated with GroupHigh correlation
Monthly Charge is highly overall correlated with Total Charges and 1 other fieldsHigh correlation
Total Charges is highly overall correlated with Account Length (months) and 3 other fieldsHigh correlation
Churn Label is highly overall correlated with Customer Service Calls and 2 other fieldsHigh correlation
Intl Active is highly overall correlated with Intl Calls and 2 other fieldsHigh correlation
Unlimited Data Plan is highly overall correlated with Monthly ChargeHigh correlation
Under 30 is highly overall correlated with Avg Monthly GB Download and 1 other fieldsHigh correlation
Senior is highly overall correlated with AgeHigh correlation
Group is highly overall correlated with Number of Customers in GroupHigh correlation
Churn Category is highly overall correlated with Churn Label and 1 other fieldsHigh correlation
Churn Reason is highly overall correlated with Churn Label and 1 other fieldsHigh correlation
Intl Plan is highly imbalanced (53.8%)Imbalance
Churn Category is highly imbalanced (61.3%)Imbalance
Churn Reason is highly imbalanced (62.4%)Imbalance
Customer ID is uniformly distributedUniform
Phone Number is uniformly distributedUniform
Customer ID has unique valuesUnique
Intl Calls has 3943 (61.5%) zerosZeros
Intl Mins has 3943 (61.5%) zerosZeros
Extra International Charges has 4272 (66.6%) zerosZeros
Customer Service Calls has 3857 (60.2%) zerosZeros
Avg Monthly GB Download has 1448 (22.6%) zerosZeros
Extra Data Charges has 5760 (89.8%) zerosZeros
Number of Customers in Group has 4946 (77.1%) zerosZeros

Reproduction

Analysis started2024-10-01 12:07:50.754743
Analysis finished2024-10-01 12:08:56.779617
Duration1 minute and 6.02 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Customer ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct6411
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
4444-BZPU
 
1
2875-PAMY
 
1
6103-KXHN
 
1
0627-LLPX
 
1
2181-OLAS
 
1
Other values (6406)
6406 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters57699
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6411 ?
Unique (%)100.0%

Sample

1st row4444-BZPU
2nd row5676-PTZX
3rd row8532-ZEKQ
4th row1314-SMPJ
5th row2956-TXCJ

Common Values

ValueCountFrequency (%)
4444-BZPU 1
 
< 0.1%
2875-PAMY 1
 
< 0.1%
6103-KXHN 1
 
< 0.1%
0627-LLPX 1
 
< 0.1%
2181-OLAS 1
 
< 0.1%
5707-SGWL 1
 
< 0.1%
6662-PNJU 1
 
< 0.1%
2786-JMVY 1
 
< 0.1%
2626-EKLI 1
 
< 0.1%
5660-KCQO 1
 
< 0.1%
Other values (6401) 6401
99.8%

Length

2024-10-01T14:08:56.921103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4444-bzpu 1
 
< 0.1%
1260-hoay 1
 
< 0.1%
1314-smpj 1
 
< 0.1%
2956-txcj 1
 
< 0.1%
9152-depy 1
 
< 0.1%
1958-sdso 1
 
< 0.1%
8787-qzuc 1
 
< 0.1%
7768-oqje 1
 
< 0.1%
7716-rheb 1
 
< 0.1%
1133-qycq 1
 
< 0.1%
Other values (6401) 6401
99.8%

Most occurring characters

ValueCountFrequency (%)
- 6411
 
11.1%
5 2633
 
4.6%
7 2605
 
4.5%
0 2587
 
4.5%
2 2584
 
4.5%
3 2581
 
4.5%
1 2580
 
4.5%
8 2568
 
4.5%
4 2543
 
4.4%
6 2485
 
4.3%
Other values (27) 28122
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25644
44.4%
Uppercase Letter 25644
44.4%
Dash Punctuation 6411
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1080
 
4.2%
Z 1055
 
4.1%
M 1036
 
4.0%
D 1023
 
4.0%
V 1022
 
4.0%
Y 1020
 
4.0%
B 1010
 
3.9%
E 1006
 
3.9%
C 1003
 
3.9%
Q 1001
 
3.9%
Other values (16) 15388
60.0%
Decimal Number
ValueCountFrequency (%)
5 2633
10.3%
7 2605
10.2%
0 2587
10.1%
2 2584
10.1%
3 2581
10.1%
1 2580
10.1%
8 2568
10.0%
4 2543
9.9%
6 2485
9.7%
9 2478
9.7%
Dash Punctuation
ValueCountFrequency (%)
- 6411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32055
55.6%
Latin 25644
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1080
 
4.2%
Z 1055
 
4.1%
M 1036
 
4.0%
D 1023
 
4.0%
V 1022
 
4.0%
Y 1020
 
4.0%
B 1010
 
3.9%
E 1006
 
3.9%
C 1003
 
3.9%
Q 1001
 
3.9%
Other values (16) 15388
60.0%
Common
ValueCountFrequency (%)
- 6411
20.0%
5 2633
8.2%
7 2605
8.1%
0 2587
8.1%
2 2584
8.1%
3 2581
8.1%
1 2580
8.0%
8 2568
8.0%
4 2543
 
7.9%
6 2485
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 6411
 
11.1%
5 2633
 
4.6%
7 2605
 
4.5%
0 2587
 
4.5%
2 2584
 
4.5%
3 2581
 
4.5%
1 2580
 
4.5%
8 2568
 
4.5%
4 2543
 
4.4%
6 2485
 
4.3%
Other values (27) 28122
48.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
4640 
True
1771 
ValueCountFrequency (%)
False 4640
72.4%
True 1771
 
27.6%
2024-10-01T14:08:57.127199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Account Length (months)
Real number (ℝ)

Distinct77
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.768523
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:08:57.370598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median26
Q352
95-th percentile71
Maximum77
Range76
Interquartile range (IQR)44

Descriptive statistics

Standard deviation23.884761
Coefficient of variation (CV)0.7762726
Kurtosis-1.2914268
Mean30.768523
Median Absolute Deviation (MAD)21
Skewness0.3226624
Sum197257
Variance570.48182
MonotonicityNot monotonic
2024-10-01T14:08:57.666403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 591
 
9.2%
2 225
 
3.5%
3 188
 
2.9%
4 161
 
2.5%
5 138
 
2.2%
71 125
 
1.9%
7 125
 
1.9%
9 122
 
1.9%
11 119
 
1.9%
8 115
 
1.8%
Other values (67) 4502
70.2%
ValueCountFrequency (%)
1 591
9.2%
2 225
 
3.5%
3 188
 
2.9%
4 161
 
2.5%
5 138
 
2.2%
6 97
 
1.5%
7 125
 
1.9%
8 115
 
1.8%
9 122
 
1.9%
10 103
 
1.6%
ValueCountFrequency (%)
77 4
 
0.1%
76 3
 
< 0.1%
75 14
 
0.2%
74 26
 
0.4%
73 73
1.1%
72 98
1.5%
71 125
1.9%
70 111
1.7%
69 77
1.2%
68 77
1.2%

Local Calls
Real number (ℝ)

Distinct430
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.78131
Minimum1
Maximum436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:08:57.962662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q128
median91
Q3184.5
95-th percentile339
Maximum436
Range435
Interquartile range (IQR)156.5

Descriptive statistics

Standard deviation107.23408
Coefficient of variation (CV)0.89524878
Kurtosis-0.074479461
Mean119.78131
Median Absolute Deviation (MAD)72
Skewness0.90528392
Sum767918
Variance11499.147
MonotonicityNot monotonic
2024-10-01T14:08:58.217546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 143
 
2.2%
4 134
 
2.1%
5 126
 
2.0%
2 115
 
1.8%
3 109
 
1.7%
7 91
 
1.4%
8 68
 
1.1%
9 66
 
1.0%
10 63
 
1.0%
12 48
 
0.7%
Other values (420) 5448
85.0%
ValueCountFrequency (%)
1 20
 
0.3%
2 115
1.8%
3 109
1.7%
4 134
2.1%
5 126
2.0%
6 143
2.2%
7 91
1.4%
8 68
1.1%
9 66
1.0%
10 63
1.0%
ValueCountFrequency (%)
436 1
 
< 0.1%
435 2
< 0.1%
434 2
< 0.1%
433 2
< 0.1%
432 2
< 0.1%
431 1
 
< 0.1%
430 3
< 0.1%
429 2
< 0.1%
428 2
< 0.1%
427 1
 
< 0.1%

Local Mins
Real number (ℝ)

Distinct4005
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.60321
Minimum4
Maximum1127.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:08:58.510369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q171.35
median234.3
Q3464.3
95-th percentile846.8
Maximum1127.6
Range1123.6
Interquartile range (IQR)392.95

Descriptive statistics

Standard deviation266.35391
Coefficient of variation (CV)0.88902219
Kurtosis-0.029813057
Mean299.60321
Median Absolute Deviation (MAD)182.6
Skewness0.90410963
Sum1920756.2
Variance70944.403
MonotonicityNot monotonic
2024-10-01T14:08:58.755748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 57
 
0.9%
15 57
 
0.9%
7 54
 
0.8%
11 49
 
0.8%
4 48
 
0.7%
13 47
 
0.7%
12 46
 
0.7%
10 46
 
0.7%
16 45
 
0.7%
5 40
 
0.6%
Other values (3995) 5922
92.4%
ValueCountFrequency (%)
4 48
0.7%
5 40
0.6%
6 34
0.5%
6.4 2
 
< 0.1%
6.9 2
 
< 0.1%
7 54
0.8%
7.4 1
 
< 0.1%
7.6 1
 
< 0.1%
7.9 2
 
< 0.1%
8 36
0.6%
ValueCountFrequency (%)
1127.6 1
< 0.1%
1125.5 1
< 0.1%
1122.6 1
< 0.1%
1114.2 1
< 0.1%
1113.5 1
< 0.1%
1113.4 1
< 0.1%
1112.1 2
< 0.1%
1109.5 1
< 0.1%
1108.9 1
< 0.1%
1108.1 1
< 0.1%

Intl Calls
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct327
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.53426
Minimum0
Maximum1050
Zeros3943
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:08:59.030280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q345
95-th percentile264
Maximum1050
Range1050
Interquartile range (IQR)45

Descriptive statistics

Standard deviation98.917958
Coefficient of variation (CV)2.0381058
Kurtosis12.858511
Mean48.53426
Median Absolute Deviation (MAD)0
Skewness2.9810591
Sum311153.14
Variance9784.7623
MonotonicityNot monotonic
2024-10-01T14:08:59.283786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3943
61.5%
4 167
 
2.6%
8 75
 
1.2%
12 68
 
1.1%
16 60
 
0.9%
36 42
 
0.7%
20 40
 
0.6%
28 39
 
0.6%
32 35
 
0.5%
24 34
 
0.5%
Other values (317) 1908
29.8%
ValueCountFrequency (%)
0 3943
61.5%
1 4
 
0.1%
2 11
 
0.2%
3 22
 
0.3%
4 167
 
2.6%
4.08 1
 
< 0.1%
4.2 1
 
< 0.1%
5 12
 
0.2%
5.880735125 2
 
< 0.1%
6 19
 
0.3%
ValueCountFrequency (%)
1050 1
< 0.1%
975 1
< 0.1%
962 1
< 0.1%
910 1
< 0.1%
896 1
< 0.1%
828 1
< 0.1%
825 1
< 0.1%
768 1
< 0.1%
737 1
< 0.1%
728 1
< 0.1%

Intl Mins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1573
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.98725
Minimum0
Maximum1372.5
Zeros3943
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:08:59.555302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3131.95
95-th percentile695.45
Maximum1372.5
Range1372.5
Interquartile range (IQR)131.95

Descriptive statistics

Standard deviation234.34468
Coefficient of variation (CV)1.8900708
Kurtosis3.2509345
Mean123.98725
Median Absolute Deviation (MAD)0
Skewness2.0259026
Sum794882.26
Variance54917.427
MonotonicityNot monotonic
2024-10-01T14:08:59.822127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3943
61.5%
9.7 12
 
0.2%
12.8 11
 
0.2%
12.1 9
 
0.1%
13.3 7
 
0.1%
14 6
 
0.1%
10.8 6
 
0.1%
335.4 6
 
0.1%
10.7 6
 
0.1%
594 6
 
0.1%
Other values (1563) 2399
37.4%
ValueCountFrequency (%)
0 3943
61.5%
1 3
 
< 0.1%
2.4 1
 
< 0.1%
3 1
 
< 0.1%
5.1 1
 
< 0.1%
5.4 1
 
< 0.1%
5.5 1
 
< 0.1%
5.7 1
 
< 0.1%
5.8 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
1372.5 1
< 0.1%
1225 1
< 0.1%
1214.1 1
< 0.1%
1164.4 1
< 0.1%
1144.6 1
< 0.1%
1134.6 1
< 0.1%
1128.6 2
< 0.1%
1079.2 1
< 0.1%
1074 2
< 0.1%
1073.1 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
3943 
True
2468 
ValueCountFrequency (%)
False 3943
61.5%
True 2468
38.5%
2024-10-01T14:09:00.062912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Intl Plan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
5783 
True
628 
ValueCountFrequency (%)
False 5783
90.2%
True 628
 
9.8%
2024-10-01T14:09:00.245294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Extra International Charges
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1232
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.109905
Minimum0
Maximum582.2
Zeros4272
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:00.461747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315.45
95-th percentile198
Maximum582.2
Range582.2
Interquartile range (IQR)15.45

Descriptive statistics

Standard deviation73.31797
Coefficient of variation (CV)2.2833443
Kurtosis10.065742
Mean32.109905
Median Absolute Deviation (MAD)0
Skewness2.995244
Sum205856.6
Variance5375.5247
MonotonicityNot monotonic
2024-10-01T14:09:00.699243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4272
66.6%
4.3 13
 
0.2%
2.6 13
 
0.2%
3.2 10
 
0.2%
2.5 10
 
0.2%
3.7 9
 
0.1%
4.7 9
 
0.1%
2.8 9
 
0.1%
5.3 9
 
0.1%
6.8 8
 
0.1%
Other values (1222) 2049
32.0%
ValueCountFrequency (%)
0 4272
66.6%
0.2 2
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.8 1
 
< 0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.4 3
 
< 0.1%
1.5 1
 
< 0.1%
1.6 3
 
< 0.1%
ValueCountFrequency (%)
582.2 1
< 0.1%
539.6 1
< 0.1%
529.2 1
< 0.1%
525.6 1
< 0.1%
520.8 1
< 0.1%
509.2 1
< 0.1%
506.3 1
< 0.1%
483 1
< 0.1%
475.2 1
< 0.1%
468 2
< 0.1%

Customer Service Calls
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93245983
Minimum0
Maximum5
Zeros3857
Zeros (%)60.2%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:00.912522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4242033
Coefficient of variation (CV)1.5273615
Kurtosis1.2914562
Mean0.93245983
Median Absolute Deviation (MAD)0
Skewness1.5147125
Sum5978
Variance2.0283549
MonotonicityNot monotonic
2024-10-01T14:09:01.096822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3857
60.2%
1 859
 
13.4%
2 827
 
12.9%
3 292
 
4.6%
4 291
 
4.5%
5 285
 
4.4%
ValueCountFrequency (%)
0 3857
60.2%
1 859
 
13.4%
2 827
 
12.9%
3 292
 
4.6%
4 291
 
4.5%
5 285
 
4.4%
ValueCountFrequency (%)
5 285
 
4.4%
4 291
 
4.5%
3 292
 
4.6%
2 827
 
12.9%
1 859
 
13.4%
0 3857
60.2%

Avg Monthly GB Download
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6657308
Minimum0
Maximum43
Zeros1448
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:01.320870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q39
95-th percentile24
Maximum43
Range43
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.4499334
Coefficient of variation (CV)1.1176469
Kurtosis3.7132669
Mean6.6657308
Median Absolute Deviation (MAD)4
Skewness1.7939798
Sum42734
Variance55.501507
MonotonicityNot monotonic
2024-10-01T14:09:01.554385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 1448
22.6%
5 503
 
7.8%
3 452
 
7.1%
4 449
 
7.0%
6 410
 
6.4%
2 410
 
6.4%
1 357
 
5.6%
7 313
 
4.9%
8 248
 
3.9%
9 240
 
3.7%
Other values (26) 1581
24.7%
ValueCountFrequency (%)
0 1448
22.6%
1 357
 
5.6%
2 410
 
6.4%
3 452
 
7.1%
4 449
 
7.0%
5 503
 
7.8%
6 410
 
6.4%
7 313
 
4.9%
8 248
 
3.9%
9 240
 
3.7%
ValueCountFrequency (%)
43 8
 
0.1%
41 7
 
0.1%
38 19
0.3%
37 19
0.3%
36 8
 
0.1%
35 19
0.3%
30 47
0.7%
29 24
0.4%
28 17
 
0.3%
27 32
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
True
4291 
False
2120 
ValueCountFrequency (%)
True 4291
66.9%
False 2120
33.1%
2024-10-01T14:09:01.787608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Extra Data Charges
Real number (ℝ)

Distinct91
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2728124
Minimum0
Maximum99
Zeros5760
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:02.001109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile27.5
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.369278
Coefficient of variation (CV)3.7794034
Kurtosis20.566712
Mean3.2728124
Median Absolute Deviation (MAD)0
Skewness4.4362222
Sum20982
Variance152.99904
MonotonicityNot monotonic
2024-10-01T14:09:02.261097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5760
89.8%
6 42
 
0.7%
5 40
 
0.6%
4 33
 
0.5%
7 19
 
0.3%
19 16
 
0.2%
13 16
 
0.2%
38 14
 
0.2%
12 13
 
0.2%
23 12
 
0.2%
Other values (81) 446
 
7.0%
ValueCountFrequency (%)
0 5760
89.8%
3 6
 
0.1%
4 33
 
0.5%
5 40
 
0.6%
6 42
 
0.7%
7 19
 
0.3%
8 6
 
0.1%
9 7
 
0.1%
10 6
 
0.1%
11 9
 
0.1%
ValueCountFrequency (%)
99 3
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 1
 
< 0.1%
90 2
< 0.1%
88 3
< 0.1%
87 1
 
< 0.1%
83 3
< 0.1%

State
Categorical

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
WV
 
203
MN
 
163
NY
 
159
OH
 
154
AL
 
153
Other values (46)
5579 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters12822
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowOH
4th rowMO
5th rowWV

Common Values

ValueCountFrequency (%)
WV 203
 
3.2%
MN 163
 
2.5%
NY 159
 
2.5%
OH 154
 
2.4%
AL 153
 
2.4%
OR 151
 
2.4%
VA 150
 
2.3%
WI 149
 
2.3%
CT 146
 
2.3%
ID 144
 
2.2%
Other values (41) 4839
75.5%

Length

2024-10-01T14:09:02.504388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 203
 
3.2%
mn 163
 
2.5%
ny 159
 
2.5%
oh 154
 
2.4%
al 153
 
2.4%
or 151
 
2.4%
va 150
 
2.3%
wi 149
 
2.3%
ct 146
 
2.3%
id 144
 
2.2%
Other values (41) 4839
75.5%

Most occurring characters

ValueCountFrequency (%)
N 1405
 
11.0%
A 1325
 
10.3%
M 1183
 
9.2%
I 1003
 
7.8%
T 801
 
6.2%
D 732
 
5.7%
C 682
 
5.3%
O 660
 
5.1%
V 619
 
4.8%
W 617
 
4.8%
Other values (14) 3795
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12822
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1405
 
11.0%
A 1325
 
10.3%
M 1183
 
9.2%
I 1003
 
7.8%
T 801
 
6.2%
D 732
 
5.7%
C 682
 
5.3%
O 660
 
5.1%
V 619
 
4.8%
W 617
 
4.8%
Other values (14) 3795
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 12822
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1405
 
11.0%
A 1325
 
10.3%
M 1183
 
9.2%
I 1003
 
7.8%
T 801
 
6.2%
D 732
 
5.7%
C 682
 
5.3%
O 660
 
5.1%
V 619
 
4.8%
W 617
 
4.8%
Other values (14) 3795
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1405
 
11.0%
A 1325
 
10.3%
M 1183
 
9.2%
I 1003
 
7.8%
T 801
 
6.2%
D 732
 
5.7%
C 682
 
5.3%
O 660
 
5.1%
V 619
 
4.8%
W 617
 
4.8%
Other values (14) 3795
29.6%

Phone Number
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct6402
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
333-7803
 
2
313-7716
 
2
359-9794
 
2
365-9011
 
2
312-3187
 
2
Other values (6397)
6401 

Length

Max length9
Median length8
Mean length8.0274528
Min length7

Characters and Unicode

Total characters51464
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6393 ?
Unique (%)99.7%

Sample

1st row382-4657
2nd row371-7191
3rd row375-9999
4th row329-9001
5th row330-8173

Common Values

ValueCountFrequency (%)
333-7803 2
 
< 0.1%
313-7716 2
 
< 0.1%
359-9794 2
 
< 0.1%
365-9011 2
 
< 0.1%
312-3187 2
 
< 0.1%
329-6144 2
 
< 0.1%
334-9818 2
 
< 0.1%
390-3401 2
 
< 0.1%
277-4048 2
 
< 0.1%
319-7505 1
 
< 0.1%
Other values (6392) 6392
99.7%

Length

2024-10-01T14:09:02.726928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
333-7803 2
 
< 0.1%
359-9794 2
 
< 0.1%
365-9011 2
 
< 0.1%
312-3187 2
 
< 0.1%
329-6144 2
 
< 0.1%
334-9818 2
 
< 0.1%
390-3401 2
 
< 0.1%
277-4048 2
 
< 0.1%
313-7716 2
 
< 0.1%
351-7269 1
 
< 0.1%
Other values (6392) 6392
99.7%

Most occurring characters

ValueCountFrequency (%)
3 8692
16.9%
- 6411
12.5%
4 4741
9.2%
2 4614
9.0%
9 4012
7.8%
5 4007
7.8%
1 3916
7.6%
8 3909
7.6%
6 3863
7.5%
7 3859
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45053
87.5%
Dash Punctuation 6411
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8692
19.3%
4 4741
10.5%
2 4614
10.2%
9 4012
8.9%
5 4007
8.9%
1 3916
8.7%
8 3909
8.7%
6 3863
8.6%
7 3859
8.6%
0 3440
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 6411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8692
16.9%
- 6411
12.5%
4 4741
9.2%
2 4614
9.0%
9 4012
7.8%
5 4007
7.8%
1 3916
7.6%
8 3909
7.6%
6 3863
7.5%
7 3859
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8692
16.9%
- 6411
12.5%
4 4741
9.2%
2 4614
9.0%
9 4012
7.8%
5 4007
7.8%
1 3916
7.6%
8 3909
7.6%
6 3863
7.5%
7 3859
7.5%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
Male
3236 
Female
3168 
Prefer not to say
 
7

Length

Max length17
Median length4
Mean length5.0024957
Min length4

Characters and Unicode

Total characters32071
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3236
50.5%
Female 3168
49.4%
Prefer not to say 7
 
0.1%

Length

2024-10-01T14:09:02.961527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T14:09:03.160013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male 3236
50.3%
female 3168
49.3%
prefer 7
 
0.1%
not 7
 
0.1%
to 7
 
0.1%
say 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 9586
29.9%
a 6411
20.0%
l 6404
20.0%
M 3236
 
10.1%
F 3168
 
9.9%
m 3168
 
9.9%
21
 
0.1%
r 14
 
< 0.1%
o 14
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25639
79.9%
Uppercase Letter 6411
 
20.0%
Space Separator 21
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9586
37.4%
a 6411
25.0%
l 6404
25.0%
m 3168
 
12.4%
r 14
 
0.1%
o 14
 
0.1%
t 14
 
0.1%
f 7
 
< 0.1%
n 7
 
< 0.1%
s 7
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 3236
50.5%
F 3168
49.4%
P 7
 
0.1%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32050
99.9%
Common 21
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9586
29.9%
a 6411
20.0%
l 6404
20.0%
M 3236
 
10.1%
F 3168
 
9.9%
m 3168
 
9.9%
r 14
 
< 0.1%
o 14
 
< 0.1%
t 14
 
< 0.1%
P 7
 
< 0.1%
Other values (4) 28
 
0.1%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9586
29.9%
a 6411
20.0%
l 6404
20.0%
M 3236
 
10.1%
F 3168
 
9.9%
m 3168
 
9.9%
21
 
0.1%
r 14
 
< 0.1%
o 14
 
< 0.1%
t 14
 
< 0.1%
Other values (5) 35
 
0.1%

Age
Real number (ℝ)

Distinct67
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.448136
Minimum19
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:03.380571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q133
median47
Q360
95-th percentile77
Maximum85
Range66
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.9597
Coefficient of variation (CV)0.3574366
Kurtosis-0.92755423
Mean47.448136
Median Absolute Deviation (MAD)14
Skewness0.21188001
Sum304190
Variance287.63144
MonotonicityNot monotonic
2024-10-01T14:09:03.624449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 182
 
2.8%
48 175
 
2.7%
21 140
 
2.2%
47 136
 
2.1%
45 132
 
2.1%
41 132
 
2.1%
25 131
 
2.0%
43 130
 
2.0%
38 129
 
2.0%
67 129
 
2.0%
Other values (57) 4995
77.9%
ValueCountFrequency (%)
19 48
 
0.7%
20 62
1.0%
21 140
2.2%
22 115
1.8%
23 117
1.8%
24 108
1.7%
25 131
2.0%
26 107
1.7%
27 116
1.8%
28 104
1.6%
ValueCountFrequency (%)
85 24
 
0.4%
84 38
0.6%
83 28
0.4%
82 37
0.6%
81 35
0.5%
80 31
0.5%
79 37
0.6%
78 45
0.7%
77 65
1.0%
76 59
0.9%

Under 30
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
5181 
True
1230 
ValueCountFrequency (%)
False 5181
80.8%
True 1230
 
19.2%
2024-10-01T14:09:03.861978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Senior
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
5209 
True
1202 
ValueCountFrequency (%)
False 5209
81.3%
True 1202
 
18.7%
2024-10-01T14:09:04.064867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Group
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
4946 
True
1465 
ValueCountFrequency (%)
False 4946
77.1%
True 1465
 
22.9%
2024-10-01T14:09:04.238293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Number of Customers in Group
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84245827
Minimum0
Maximum6
Zeros4946
Zeros (%)77.1%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:04.410638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.704856
Coefficient of variation (CV)2.0236682
Kurtosis2.2559684
Mean0.84245827
Median Absolute Deviation (MAD)0
Skewness1.8896599
Sum5401
Variance2.906534
MonotonicityNot monotonic
2024-10-01T14:09:04.587646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4946
77.1%
2 474
 
7.4%
3 257
 
4.0%
6 257
 
4.0%
4 245
 
3.8%
5 232
 
3.6%
ValueCountFrequency (%)
0 4946
77.1%
2 474
 
7.4%
3 257
 
4.0%
4 245
 
3.8%
5 232
 
3.6%
6 257
 
4.0%
ValueCountFrequency (%)
6 257
 
4.0%
5 232
 
3.6%
4 245
 
3.8%
3 257
 
4.0%
2 474
 
7.4%
0 4946
77.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 KiB
False
4314 
True
2097 
ValueCountFrequency (%)
False 4314
67.3%
True 2097
32.7%
2024-10-01T14:09:04.780021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Contract Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
Month-to-Month
3391 
Two Year
1606 
One Year
1414 

Length

Max length14
Median length14
Mean length11.173608
Min length8

Characters and Unicode

Total characters71634
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-Month
2nd rowOne Year
3rd rowOne Year
4th rowMonth-to-Month
5th rowOne Year

Common Values

ValueCountFrequency (%)
Month-to-Month 3391
52.9%
Two Year 1606
25.1%
One Year 1414
22.1%

Length

2024-10-01T14:09:04.985736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T14:09:05.204558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3391
36.0%
year 3020
32.0%
two 1606
17.0%
one 1414
15.0%

Most occurring characters

ValueCountFrequency (%)
o 11779
16.4%
t 10173
14.2%
n 8196
11.4%
M 6782
9.5%
h 6782
9.5%
- 6782
9.5%
e 4434
 
6.2%
3020
 
4.2%
Y 3020
 
4.2%
a 3020
 
4.2%
Other values (4) 7646
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49010
68.4%
Uppercase Letter 12822
 
17.9%
Dash Punctuation 6782
 
9.5%
Space Separator 3020
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11779
24.0%
t 10173
20.8%
n 8196
16.7%
h 6782
13.8%
e 4434
 
9.0%
a 3020
 
6.2%
r 3020
 
6.2%
w 1606
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
M 6782
52.9%
Y 3020
23.6%
T 1606
 
12.5%
O 1414
 
11.0%
Dash Punctuation
ValueCountFrequency (%)
- 6782
100.0%
Space Separator
ValueCountFrequency (%)
3020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61832
86.3%
Common 9802
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11779
19.1%
t 10173
16.5%
n 8196
13.3%
M 6782
11.0%
h 6782
11.0%
e 4434
 
7.2%
Y 3020
 
4.9%
a 3020
 
4.9%
r 3020
 
4.9%
T 1606
 
2.6%
Other values (2) 3020
 
4.9%
Common
ValueCountFrequency (%)
- 6782
69.2%
3020
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11779
16.4%
t 10173
14.2%
n 8196
11.4%
M 6782
9.5%
h 6782
9.5%
- 6782
9.5%
e 4434
 
6.2%
3020
 
4.2%
Y 3020
 
4.2%
a 3020
 
4.2%
Other values (4) 7646
10.7%

Payment Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
Direct Debit
3554 
Credit Card
2491 
Paper Check
366 

Length

Max length12
Median length12
Mean length11.55436
Min length11

Characters and Unicode

Total characters74075
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Debit
2nd rowPaper Check
3rd rowDirect Debit
4th rowPaper Check
5th rowDirect Debit

Common Values

ValueCountFrequency (%)
Direct Debit 3554
55.4%
Credit Card 2491
38.9%
Paper Check 366
 
5.7%

Length

2024-10-01T14:09:05.416325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T14:09:05.624590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
direct 3554
27.7%
debit 3554
27.7%
credit 2491
19.4%
card 2491
19.4%
paper 366
 
2.9%
check 366
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 10331
13.9%
i 9599
13.0%
t 9599
13.0%
r 8902
12.0%
D 7108
9.6%
6411
8.7%
C 5348
7.2%
d 4982
6.7%
c 3920
 
5.3%
b 3554
 
4.8%
Other values (5) 4321
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54842
74.0%
Uppercase Letter 12822
 
17.3%
Space Separator 6411
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10331
18.8%
i 9599
17.5%
t 9599
17.5%
r 8902
16.2%
d 4982
9.1%
c 3920
 
7.1%
b 3554
 
6.5%
a 2857
 
5.2%
p 366
 
0.7%
h 366
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
D 7108
55.4%
C 5348
41.7%
P 366
 
2.9%
Space Separator
ValueCountFrequency (%)
6411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67664
91.3%
Common 6411
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10331
15.3%
i 9599
14.2%
t 9599
14.2%
r 8902
13.2%
D 7108
10.5%
C 5348
7.9%
d 4982
7.4%
c 3920
 
5.8%
b 3554
 
5.3%
a 2857
 
4.2%
Other values (4) 1464
 
2.2%
Common
ValueCountFrequency (%)
6411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10331
13.9%
i 9599
13.0%
t 9599
13.0%
r 8902
12.0%
D 7108
9.6%
6411
8.7%
C 5348
7.2%
d 4982
6.7%
c 3920
 
5.3%
b 3554
 
4.8%
Other values (5) 4321
5.8%

Monthly Charge
Real number (ℝ)

Distinct69
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.299173
Minimum5
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:05.892627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q116
median31
Q342
95-th percentile56
Maximum75
Range70
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.701109
Coefficient of variation (CV)0.51820257
Kurtosis-0.96245704
Mean30.299173
Median Absolute Deviation (MAD)13
Skewness0.15727501
Sum194248
Variance246.52483
MonotonicityNot monotonic
2024-10-01T14:09:06.167957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 246
 
3.8%
9 228
 
3.6%
8 222
 
3.5%
7 188
 
2.9%
12 172
 
2.7%
11 164
 
2.6%
35 162
 
2.5%
13 159
 
2.5%
34 154
 
2.4%
39 152
 
2.4%
Other values (59) 4564
71.2%
ValueCountFrequency (%)
5 12
 
0.2%
6 71
 
1.1%
7 188
2.9%
8 222
3.5%
9 228
3.6%
10 246
3.8%
11 164
2.6%
12 172
2.7%
13 159
2.5%
14 58
 
0.9%
ValueCountFrequency (%)
75 1
 
< 0.1%
73 1
 
< 0.1%
72 3
 
< 0.1%
70 4
 
0.1%
69 5
 
0.1%
68 3
 
< 0.1%
67 7
 
0.1%
66 7
 
0.1%
65 17
0.3%
64 18
0.3%

Total Charges
Real number (ℝ)

Distinct2472
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987.78864
Minimum6
Maximum4025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.2 KiB
2024-10-01T14:09:06.452376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile22
Q1169
median595
Q31587
95-th percentile3128.5
Maximum4025
Range4019
Interquartile range (IQR)1418

Descriptive statistics

Standard deviation1008.3826
Coefficient of variation (CV)1.0208486
Kurtosis0.13593241
Mean987.78864
Median Absolute Deviation (MAD)519
Skewness1.0805561
Sum6332713
Variance1016835.6
MonotonicityNot monotonic
2024-10-01T14:09:06.729259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 34
 
0.5%
26 32
 
0.5%
13 32
 
0.5%
12 29
 
0.5%
27 27
 
0.4%
16 24
 
0.4%
7 23
 
0.4%
37 23
 
0.4%
25 22
 
0.3%
9 22
 
0.3%
Other values (2462) 6143
95.8%
ValueCountFrequency (%)
6 9
 
0.1%
7 23
0.4%
8 21
0.3%
9 22
0.3%
10 34
0.5%
11 19
0.3%
12 29
0.5%
13 32
0.5%
14 11
 
0.2%
15 9
 
0.1%
ValueCountFrequency (%)
4025 1
< 0.1%
4008 1
< 0.1%
4006 1
< 0.1%
3989 2
< 0.1%
3987 1
< 0.1%
3981 1
< 0.1%
3979 1
< 0.1%
3974 1
< 0.1%
3971 1
< 0.1%
3969 1
< 0.1%

Churn Category
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
Competitor
5461 
Dissatisfaction
 
283
Attitude
 
278
Price
 
199
Other
 
190

Length

Max length15
Median length10
Mean length9.8306036
Min length5

Characters and Unicode

Total characters63024
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor
2nd rowCompetitor
3rd rowCompetitor
4th rowCompetitor
5th rowCompetitor

Common Values

ValueCountFrequency (%)
Competitor 5461
85.2%
Dissatisfaction 283
 
4.4%
Attitude 278
 
4.3%
Price 199
 
3.1%
Other 190
 
3.0%

Length

2024-10-01T14:09:07.012857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T14:09:07.254303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
competitor 5461
85.2%
dissatisfaction 283
 
4.4%
attitude 278
 
4.3%
price 199
 
3.1%
other 190
 
3.0%

Most occurring characters

ValueCountFrequency (%)
t 12512
19.9%
o 11205
17.8%
i 6787
10.8%
e 6128
9.7%
r 5850
9.3%
C 5461
8.7%
m 5461
8.7%
p 5461
8.7%
s 849
 
1.3%
a 566
 
0.9%
Other values (10) 2744
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56613
89.8%
Uppercase Letter 6411
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12512
22.1%
o 11205
19.8%
i 6787
12.0%
e 6128
10.8%
r 5850
10.3%
m 5461
9.6%
p 5461
9.6%
s 849
 
1.5%
a 566
 
1.0%
c 482
 
0.9%
Other values (5) 1312
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 5461
85.2%
D 283
 
4.4%
A 278
 
4.3%
P 199
 
3.1%
O 190
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12512
19.9%
o 11205
17.8%
i 6787
10.8%
e 6128
9.7%
r 5850
9.3%
C 5461
8.7%
m 5461
8.7%
p 5461
8.7%
s 849
 
1.3%
a 566
 
0.9%
Other values (10) 2744
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 12512
19.9%
o 11205
17.8%
i 6787
10.8%
e 6128
9.7%
r 5850
9.3%
C 5461
8.7%
m 5461
8.7%
p 5461
8.7%
s 849
 
1.3%
a 566
 
0.9%
Other values (10) 2744
 
4.4%

Churn Reason
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size100.2 KiB
Competitor made better offer
4965 
Competitor had better devices
 
293
Attitude of support person
 
198
Don't know
 
122
Competitor offered more data
 
110
Other values (15)
723 

Length

Max length41
Median length28
Mean length27.243488
Min length5

Characters and Unicode

Total characters174658
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor made better offer
2nd rowCompetitor made better offer
3rd rowCompetitor made better offer
4th rowCompetitor made better offer
5th rowCompetitor made better offer

Common Values

ValueCountFrequency (%)
Competitor made better offer 4965
77.4%
Competitor had better devices 293
 
4.6%
Attitude of support person 198
 
3.1%
Don't know 122
 
1.9%
Competitor offered more data 110
 
1.7%
Competitor offered higher download speeds 93
 
1.5%
Attitude of service provider 80
 
1.2%
Price too high 74
 
1.2%
Product dissatisfaction 71
 
1.1%
Network reliability 68
 
1.1%
Other values (10) 337
 
5.3%

Length

2024-10-01T14:09:07.492012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor 5461
22.0%
better 5258
21.1%
made 4965
20.0%
offer 4965
20.0%
of 408
 
1.6%
had 293
 
1.2%
devices 293
 
1.2%
attitude 278
 
1.1%
support 239
 
1.0%
offered 203
 
0.8%
Other values (37) 2506
10.1%

Most occurring characters

ValueCountFrequency (%)
e 29072
16.6%
t 23521
13.5%
18458
10.6%
o 18339
10.5%
r 17447
10.0%
f 10957
 
6.3%
m 10571
 
6.1%
i 7379
 
4.2%
d 7025
 
4.0%
p 6418
 
3.7%
Other values (27) 25471
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149587
85.6%
Space Separator 18458
 
10.6%
Uppercase Letter 6437
 
3.7%
Other Punctuation 150
 
0.1%
Dash Punctuation 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29072
19.4%
t 23521
15.7%
o 18339
12.3%
r 17447
11.7%
f 10957
 
7.3%
m 10571
 
7.1%
i 7379
 
4.9%
d 7025
 
4.7%
p 6418
 
4.3%
a 6374
 
4.3%
Other values (13) 12484
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 5461
84.8%
A 278
 
4.3%
P 186
 
2.9%
L 150
 
2.3%
D 128
 
2.0%
N 68
 
1.1%
S 60
 
0.9%
M 44
 
0.7%
E 36
 
0.6%
W 26
 
0.4%
Other Punctuation
ValueCountFrequency (%)
' 122
81.3%
/ 28
 
18.7%
Space Separator
ValueCountFrequency (%)
18458
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 156024
89.3%
Common 18634
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29072
18.6%
t 23521
15.1%
o 18339
11.8%
r 17447
11.2%
f 10957
 
7.0%
m 10571
 
6.8%
i 7379
 
4.7%
d 7025
 
4.5%
p 6418
 
4.1%
a 6374
 
4.1%
Other values (23) 18921
12.1%
Common
ValueCountFrequency (%)
18458
99.1%
' 122
 
0.7%
/ 28
 
0.2%
- 26
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29072
16.6%
t 23521
13.5%
18458
10.6%
o 18339
10.5%
r 17447
10.0%
f 10957
 
6.3%
m 10571
 
6.1%
i 7379
 
4.2%
d 7025
 
4.0%
p 6418
 
3.7%
Other values (27) 25471
14.6%

Interactions

2024-10-01T14:08:49.675499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:57.229412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:00.651135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:03.821384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:08.138062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:12.338921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:18.320729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:24.406511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:29.700280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:34.240515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:38.090995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:41.645107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:45.214843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:50.025982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:57.496254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:00.912786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:04.076150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:08.492336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:12.688816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:19.042602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:25.014355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:30.083415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:34.530477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:38.382204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:41.909323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:45.625587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:50.305290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:57.754768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:01.129224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:04.304439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:08.787898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:12.989017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:19.390270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:25.671722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:30.524481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:34.783395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:38.633052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:42.146092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:45.981809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:50.614382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:57.994889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:01.354270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:04.529295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:09.073298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:13.330212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:19.711480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:26.136834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:30.923054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:35.053575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:38.925910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:42.380905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:46.319798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:50.884634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:58.250857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:01.578539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:04.762060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:09.533608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:13.771520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:20.018498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:26.476403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:31.230029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:35.392409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:39.191144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:42.611806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:46.733221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:51.207307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:58.526742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:01.823743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:04.998523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:09.918261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:14.251621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:20.325552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:26.772224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:31.573299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:35.791074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:39.493006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:42.860680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:47.187490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:51.504200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:58.795561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:02.088110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:05.262108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:10.325526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:14.754393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:20.639238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:27.104322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:31.955103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:36.030609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:39.785177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:43.149547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:47.496260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:51.790735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:59.055247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:02.320888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:05.504597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:10.583228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:15.708429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:21.387701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:27.410000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:32.305167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:36.275274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:40.025632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:43.426568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:47.766281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:52.074315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:59.311869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:02.578013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:05.739639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:10.838997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:16.108516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:21.865939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:27.684177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:32.612607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:36.571549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:40.268475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:43.660983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:48.064734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:52.516317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:59.562281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:02.804922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:05.978930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:11.134894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:16.603123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:22.339949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:28.088440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:33.000381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:36.805858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:40.542649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:43.912152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:48.380946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:52.934510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:07:59.838027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:03.062136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:07.397738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:11.416280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:17.126401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:22.742074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:28.554252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:33.421090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:37.045558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:40.817750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:44.178905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:48.691523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:53.226676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:00.110595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:03.311866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:07.637904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:11.717307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:17.529008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:23.247569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:28.942610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:33.673442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:37.337712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:41.140481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:44.444069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:48.998819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:53.494203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:00.388186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:03.569745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:07.895696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:12.027251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:17.881298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:23.644210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:29.358460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:33.962122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:37.588561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:41.395489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:44.795798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-10-01T14:08:49.322061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2024-10-01T14:09:08.165236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Account Length (months)Local CallsLocal MinsIntl CallsIntl MinsExtra International ChargesCustomer Service CallsAvg Monthly GB DownloadExtra Data ChargesAgeNumber of Customers in GroupMonthly ChargeTotal ChargesChurn LabelIntl ActiveIntl PlanUnlimited Data PlanStateGenderUnder 30SeniorGroupDevice Protection & Online BackupContract TypePayment MethodChurn CategoryChurn Reason
Account Length (months)1.0000.8950.9090.1050.1080.014-0.2110.0510.0110.0130.1490.1510.8720.3570.0570.0000.0130.0260.0000.0000.0190.1500.3390.4930.0960.1200.101
Local Calls0.8951.0000.9850.0960.0980.015-0.1960.0480.0090.0150.1310.1450.8030.3200.0270.0000.0260.0310.0210.0210.0110.1230.2960.3920.0760.1070.085
Local Mins0.9090.9851.0000.0970.0990.014-0.1990.0480.0060.0140.1350.1480.8140.3220.0440.0000.0180.0260.0000.0000.0320.1290.3030.4030.0830.1050.085
Intl Calls0.1050.0960.0971.0000.9930.8000.0630.0290.0190.0140.0110.0400.0990.0990.5910.1990.0380.0050.0000.0110.0220.0570.1100.1930.0340.0200.000
Intl Mins0.1080.0980.0990.9931.0000.8020.0630.0290.0170.0150.0110.0400.1010.1020.7230.2200.0000.0090.0000.0000.0320.0650.1270.2070.0280.0390.000
Extra International Charges0.0140.0150.0140.8000.8021.0000.1600.0490.0260.017-0.0240.0700.0380.0740.5540.1470.0220.0020.0000.0000.0000.0350.0930.1360.0270.0250.000
Customer Service Calls-0.211-0.196-0.1990.0630.0630.1601.0000.0470.0350.070-0.1610.140-0.1220.6540.1110.0000.0980.0000.0160.0240.0950.1720.0390.2050.1010.2270.265
Avg Monthly GB Download0.0510.0480.0480.0290.0290.0490.0471.0000.135-0.2090.0060.4860.2940.1090.0190.0310.4210.0000.0300.5070.1640.1270.2240.0780.0930.0400.039
Extra Data Charges0.0110.0090.0060.0190.0170.0260.0350.1351.0000.031-0.0370.1260.0760.0000.0000.0000.4110.0000.0000.0320.0380.0200.0760.0090.0210.0000.007
Age0.0130.0150.0140.0140.0150.0170.070-0.2090.0311.000-0.1140.1460.0770.1310.0330.0000.1200.0000.0000.9000.9610.1450.0510.0270.1070.0480.037
Number of Customers in Group0.1490.1310.1350.0110.011-0.024-0.1610.006-0.037-0.1141.000-0.280-0.0100.2530.0160.0220.1270.0000.0000.0340.1351.0000.0060.1280.0720.0750.086
Monthly Charge0.1510.1450.1480.0400.0400.0700.1400.4860.1260.146-0.2801.0000.5690.2470.0310.0000.6890.0250.0000.0400.1880.2760.4120.1180.2180.0760.079
Total Charges0.8720.8030.8140.0990.1010.038-0.1220.2940.0760.077-0.0100.5691.0000.1770.0160.0260.3150.0140.0000.0000.0910.0640.4850.3030.1170.0640.045
Churn Label0.3570.3200.3220.0990.1020.0740.6540.1090.0000.1310.2530.2470.1771.0000.1360.0060.1740.0810.0000.0400.1290.2530.0500.4480.2280.6750.872
Intl Active0.0570.0270.0440.5910.7230.5540.1110.0190.0000.0330.0160.0310.0160.1361.0000.2270.0000.0330.0000.0000.0240.0120.0050.0560.0260.1550.173
Intl Plan0.0000.0000.0000.1990.2200.1470.0000.0310.0000.0000.0220.0000.0260.0060.2271.0000.0000.1050.0000.0200.0000.0010.0180.0000.0270.0000.000
Unlimited Data Plan0.0130.0260.0180.0380.0000.0220.0980.4210.4110.1200.1270.6890.3150.1740.0000.0001.0000.0260.0000.0290.1110.1270.2920.1490.2080.1090.172
State0.0260.0310.0260.0050.0090.0020.0000.0000.0000.0000.0000.0250.0140.0810.0330.1050.0261.0000.0000.0000.0200.0000.0210.0000.0000.0230.004
Gender0.0000.0210.0000.0000.0000.0000.0160.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0150.0000.0000.0000.000
Under 300.0000.0210.0000.0110.0000.0000.0240.5070.0320.9000.0340.0400.0000.0400.0000.0200.0290.0000.0001.0000.2330.0320.0000.0000.0400.0320.028
Senior0.0190.0110.0320.0220.0320.0000.0950.1640.0380.9610.1350.1880.0910.1290.0240.0000.1110.0200.0000.2331.0000.1360.0350.0420.1360.0920.117
Group0.1500.1230.1290.0570.0650.0350.1720.1270.0200.1451.0000.2760.0640.2530.0120.0010.1270.0000.0000.0320.1361.0000.0140.1820.1040.1560.214
Device Protection & Online Backup0.3390.2960.3030.1100.1270.0930.0390.2240.0760.0510.0060.4120.4850.0500.0050.0180.2920.0210.0150.0000.0350.0141.0000.2090.0770.0600.056
Contract Type0.4930.3920.4030.1930.2070.1360.2050.0780.0090.0270.1280.1180.3030.4480.0560.0000.1490.0000.0000.0000.0420.1820.2091.0000.1190.2170.278
Payment Method0.0960.0760.0830.0340.0280.0270.1010.0930.0210.1070.0720.2180.1170.2280.0260.0270.2080.0000.0000.0400.1360.1040.0770.1191.0000.0960.141
Churn Category0.1200.1070.1050.0200.0390.0250.2270.0400.0000.0480.0750.0760.0640.6750.1550.0000.1090.0230.0000.0320.0920.1560.0600.2170.0961.0000.991
Churn Reason0.1010.0850.0850.0000.0000.0000.2650.0390.0070.0370.0860.0790.0450.8720.1730.0000.1720.0040.0000.0280.1170.2140.0560.2780.1410.9911.000

Missing values

2024-10-01T14:08:54.912224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-01T14:08:56.448910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Customer IDChurn LabelAccount Length (months)Local CallsLocal MinsIntl CallsIntl MinsIntl ActiveIntl PlanExtra International ChargesCustomer Service CallsAvg Monthly GB DownloadUnlimited Data PlanExtra Data ChargesStatePhone NumberGenderAgeUnder 30SeniorGroupNumber of Customers in GroupDevice Protection & Online BackupContract TypePayment MethodMonthly ChargeTotal ChargesChurn CategoryChurn Reason
04444-BZPUNo138.00.00.0Nono0.003Yes0KS382-4657Female35NoNoNo0NoMonth-to-MonthDirect Debit1010CompetitorCompetitor made better offer
15676-PTZXNo33179431.30.00.0Nono0.003Yes0OH371-7191Male49NoNoNo0YesOne YearPaper Check21703CompetitorCompetitor made better offer
28532-ZEKQNo4482217.60.00.0Noyes0.003Yes0OH375-9999Male51NoNoNo0YesOne YearDirect Debit231014CompetitorCompetitor made better offer
31314-SMPJNo1047111.660.071.0Yesyes0.002Yes0MO329-9001Female41NoNoNo0NoMonth-to-MonthPaper Check17177CompetitorCompetitor made better offer
42956-TXCJNo62184621.2310.0694.4Yesyes0.003Yes0WV330-8173Male51NoNoNo0NoOne YearDirect Debit281720CompetitorCompetitor made better offer
59152-DEPYNo1768120.70.00.0Nono0.000No0RI344-9403Male23YesNoNo0NoTwo YearCredit Card9156CompetitorCompetitor made better offer
61958-SDSONo57428849.20.00.0Nono0.005Yes0IA363-1107Male38NoNoNo0YesOne YearCredit Card472671CompetitorCompetitor made better offer
78787-QZUCNo2554203.70.00.0Nono0.0012Yes0IA366-9238Male29YesNoNo0YesMonth-to-MonthDirect Debit471197CompetitorCompetitor made better offer
87768-OQJENo70171627.40.00.0Nono0.001Yes0NY351-7269Female47NoNoNo0YesTwo YearCredit Card523593CompetitorCompetitor made better offer
97716-RHEBNo50206445.80.00.0Nono0.000No0ID350-8884Female61NoNoNo0NoOne YearPaper Check11539CompetitorCompetitor made better offer
Customer IDChurn LabelAccount Length (months)Local CallsLocal MinsIntl CallsIntl MinsIntl ActiveIntl PlanExtra International ChargesCustomer Service CallsAvg Monthly GB DownloadUnlimited Data PlanExtra Data ChargesStatePhone NumberGenderAgeUnder 30SeniorGroupNumber of Customers in GroupDevice Protection & Online BackupContract TypePayment MethodMonthly ChargeTotal ChargesChurn CategoryChurn Reason
66772424-WMEUYes3657144.20.00.0Nono0.006Yes0KY336-3020Male56NoNoYes2YesMonth-to-MonthDirect Debit16570CompetitorCompetitor offered more data
66787223-IAZOYes1167161.80.00.0Noyes0.028No0TX276-9423Female32NoNoYes6NoMonth-to-MonthDirect Debit22241PriceExtra data charges
66792044-UCGHYes2289285.70.00.0Nono0.046Yes0AK316-9298Male85NoYesYes2NoMonth-to-MonthPaper Check17377OtherMoved
66803059-FTFJYes1713.00.00.0Nono0.016Yes0MO286-7714Male33NoNoYes2NoMonth-to-MonthPaper Check1313PriceExtra data charges
66818708-NXSFYes1412.00.00.0Nono0.016Yes0WV337-5104Male76NoYesYes5NoMonth-to-MonthCredit Card1414DissatisfactionService dissatisfaction
66822940-QHVUYes3616.80.00.0Nono0.004Yes0SC362-9895Female42NoNoYes2NoMonth-to-MonthPaper Check1952CompetitorCompetitor offered higher download speeds
66833033-TMYGYes1715.00.00.0Nono0.0517Yes0KY378-9926Male24YesNoYes3YesMonth-to-MonthDirect Debit2020CompetitorCompetitor offered higher download speeds
66847029-XDVMYes62046.90.00.0Nono0.0410Yes0NE328-3647Male48NoNoYes6YesMonth-to-MonthPaper Check18108CompetitorCompetitor made better offer
66856614-NAJGYes3615.40.00.0Nono0.025Yes0MN346-8275Female45NoNoYes5NoMonth-to-MonthCredit Card1546AttitudeAttitude of support person
66865104-AGDXYes1715.00.00.0Nono0.0110No4IN257-5893Male22YesNoYes6NoMonth-to-MonthDirect Debit99CompetitorCompetitor made better offer